static bool TestFullConnect()
{
ArrDataEnumerator datas = new ArrDataEnumerator();
//and test
double[,,] temp = new double[1, 1, 2];
double[,,] otemp = new double[1, 1, 1];
datas.AddSample(temp, otemp);
temp = new double[1, 1, 2];
otemp = new double[1, 1, 1];
temp[0, 0, 0] = 1;
datas.AddSample(temp, otemp);
temp = new double[1, 1, 2];
otemp = new double[1, 1, 1];
temp[0, 0, 1] = 1;
datas.AddSample(temp, otemp);
temp = new double[1, 1, 2];
otemp = new double[1, 1, 1];
temp[0, 0, 0] = 1;
temp[0, 0, 1] = 1;
otemp[0, 0, 0] = 1;
datas.AddSample(temp, otemp);
Network network = new Network();
network.AddLayer(new TSConv2D(new Sigmoid(), 2, 1, 1, 10, 1));
network.AddLayer(new FullyConnLayar(new Sigmoid(), new Size(1, 1, 1)));
//network.AddLayer(new FullyConnLayar(new Sigmoid(), new Size(1, 1, 20)));
//network.AddLayer(new FullyConnLayar(new Sigmoid(), new Size(1, 1, 1)));
network.Compile(new Size(1, 1, 2), true);
network.Normalization();
//network.Normalization();
MomentumParallel mom = new MomentumParallel(network, 0.9, 1e-2);
mom.TrainBatch(datas, 320, 3000);
return(true);
}
static void PredictorClasses()
{
StreamReader rd = new StreamReader("data.txt");
string classes_line = rd.ReadLine();
string[] classes = classes_line.Split(' ');
List <double[, , ]> inputs = new List <double[, , ]>();
List <double[, , ]> outputs = new List <double[, , ]>();
while (!rd.EndOfStream)
{
inputs.Add(LineToArr3(rd.ReadLine()));
outputs.Add(LineToArr3(rd.ReadLine()));
}
int output_len = outputs[0].Length;
int nancount = 0;
for (int i = 0; i < inputs.Count; i++)
{
var l = inputs[i];
for (int j = 0; j < l.Length; j++)
{
if (double.IsNaN(l[0, 0, j]) || double.IsInfinity(l[0, 0, j]))
{
nancount++;
inputs.RemoveAt(i);
outputs.RemoveAt(i);
i--;
break;
}
}
}
ArrDataEnumerator enumerator = new ArrDataEnumerator(inputs, outputs);
var network = new Network();
network.LoadJSON(File.ReadAllText("network.neural"));
List <int> vals = new List <int>();
List <int> train_vals = new List <int>();
foreach (var pair in enumerator)
{
var output = network.GetOutput(pair.Key);
var true_output = pair.Value;
output.ArgMax(out int c, out int y, out int x);
true_output.ArgMax(out int tc, out int ty, out int tx);
if (vals.Count > x)
{
vals[x]++;
}
else
{
while (vals.Count <= x)
{
vals.Add(0);
}
vals[x]++;
}
if (train_vals.Count > tx)
{
train_vals[tx]++;
}
else
{
while (train_vals.Count <= tx)
{
train_vals.Add(0);
}
train_vals[tx]++;
}
}
;
Console.ReadLine();
}
static void LinePredictor()
{
StreamReader rd = new StreamReader("data.txt");
string classes_line = rd.ReadLine();
string[] classes = classes_line.Split(' ');
List <double[, , ]> inputs = new List <double[, , ]>();
List <double [, , ]> outputs = new List <double[, , ]>();
while (!rd.EndOfStream)
{
inputs.Add(LineToArr3(rd.ReadLine()));
outputs.Add(LineToArr3(rd.ReadLine()));
}
int output_len = outputs[0].Length;
int nancount = 0;
for (int i = 0; i < inputs.Count; i++)
{
var l = inputs[i];
for (int j = 0; j < l.Length; j++)
{
if (double.IsNaN(l[0, 0, j]) || double.IsInfinity(l[0, 0, j]))
{
nancount++;
inputs.RemoveAt(i);
outputs.RemoveAt(i);
i--;
break;
}
}
}
ArrDataEnumerator enumerator = new ArrDataEnumerator(inputs, outputs);
DataCategoryVisualisation visualisation = new DataCategoryVisualisation(enumerator, 2, 6, 600, classes);
visualisation.ShowDialog();
Network network = new Network();
network.loss = new MaxLoss();
network.AddLayer(new FullyConnLayar(new LeackyRelu(0.01), new NeuralNetwork.Size(1, 1, 64)));
network.AddLayer(new FullyConnLayar(new Sigmoid(), new NeuralNetwork.Size(1, 1, output_len)));
network.Compile(new NeuralNetwork.Size(1, 1, inputs[0].GetLength(2)), true);
network.AddNoise();
Optimizer parallel = new MomentumParallel(network, 0.9, 0.0005);
enumerator.SplitTrainVal(0.8, out ArrDataEnumerator train, out ArrDataEnumerator val);
Learner learner = new Learner()
{
optimizer = parallel,
train_data = train,
val_data = val
};
learner.metrics.Add(new ArgMaxMetrics());
learner.metrics.Add(new ArgmaxCount(0, 0, 4));
learner.metrics.Add(new ArgmaxTrainCount(0, 0, 4));
learner.callbacks.Add(new MetricSaveCallback("val_argmax"));
learner.Learn(8, 50, 10000);
}
